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AI Expands the Reach of Clinical Trials, Broadening Access to More Women, Minority, and Older Patients

A health care worker helps a patient with paperwork during a clinical trial of tests.
Carl Recine/Reuters
Apr 16 2021
Fellow, Research, Stanford

Pharmaceutical companies spend years and billions of dollars developing new drugs. While most drugs never make it out of the lab, a precious few reach clinical trial — a real-world test on human patients. And yet, despite that hard work and long odds, many clinical trials must be abandoned or sent back to the drawing board because they fail to enroll enough patients in the study.

“A failed or delayed clinical trial is costly on many fronts,” says James Zou, a professor of biomedical data science at Stanford and a member of the Institute for Human-Centered Artificial Intelligence, who co-led a collaboration between Stanford and researchers at Genentech that set out to see if AI could rectify the problem.

“Trial delays can add hundreds of millions of dollars to the price tag for a new drug and also be a profound emotional disappointment for the researchers working on it. But perhaps most important, Zou notes, delays mean that many needful patients cannot access new, potentially life-altering, medicines.

“The team focused on a deadly form of cancer, known as advanced non-small cell lung cancer — aNSCLC. Eight in 10 aNSCLC patients did not qualify for drug trials and almost 90 percent of clinical trials did not complete target enrollment by preset deadlines, according to the study.

“Their result, recently published in the journal Nature, is “Trial Pathfinder,” an AI algorithm that helps to design appropriate eligibility rules for clinical trials. Trial Pathfinder combs electronic health records and examines the details that allow some patients to be eligible for a trial and other patients to be excluded. Trial Pathfinder not only doubled the number of potential trial enrollees but it also broadened the pool to include more women, minority, and older patients as well.

Study co-lead Ruishan Liu is a 2016 SGF Fellow.

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